VAPSE-based analysis: a two-phased candidate gene approach for elucidating genetic predisposition to complex disorders.

The extraordinary success of linkage analysis in diseases with Mendelian inheritance has not extended readily to the genetics of common complex diseases. VAPSE-based analysis is a type of candidate gene approach that represents an alternative strategy by which genetic mechanisms can be defined despite the presence of substantial genetic heterogeneity. Recent advances in mutation screening and statistical methodology have enhanced substantially the efficiency and power of this approach. The "bread and butter" of VAPSE-based analysis is genotype-to-phenotype searches in large populations with computerized medical records.     

DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases

Background  

Domains are basic units of proteins, and thus exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "domainRBF" (domain Rank with Bayes Factor) to prioritize candidate domains for human complex diseases.     

Bacteriophage lambda display of complex cDNA libraries: a new approach to functional genomics

We describe the construction and characterization of two lambda surface displayed cDNA expression libraries derived from human brain and mouse embryo. cDNA inserts were obtained by tagged random-priming elongation of commercially available cDNA libraries and cloned into a novel lambda vector at the 3' end of the D capsid protein gene, which produced highly complex repertoires (1x10(8) and 2x10(7) phage). These libraries were affinity selected with a monoclonal antibody against the neural specific factor GAP-43 and with polyclonal antibodies that recognize the EMX1 and EMX2 homeoproteins. In both cases rapid identification of specific clones was achieved, which demonstrates the great potential of the lambda display system for generating affinity selectable cDNA libraries from complex genomes.     

Classic approach revitalizes genomics: Complete characterization of a candidate gene for thermal adaptation in two coral reef fishes

Lactate dehydrogenase-B (ldh-b) encodes a metabolic enzyme (LDH-B) which plays an important role in maintaining aerobic performance and in thermal acclimation and/or adaptation of fish. As the first step in understanding the effect this enzyme has on the ability of tropical coral reef fishes to cope with thermal stress, we characterized both coding and non-coding regions of ldh-b in two congeneric perciformes, Plectropomus leopardus and Plectropomus laevis. Ldh-b was 4666 and 4539bp in length in P. leopardus and P. laevis, respectively, with coding regions comprising 1005bp in both species. We report a high level of sequence homology between the coding regions of ldh-b in these two species, with 98.1% identity of nucleotides corresponding to 100% amino acid identity between the deduced protein sequences. Comparison between non-coding (intron) regions of both species revealed the presence of several indels, despite the high level of homology observed (95.9% identity of intron nucleotides). Potential regulatory motifs and elements, including twenty-six simple sequence repeat motifs (mono-, di-, tri- and tetranucleotide) and twenty-three putative microRNA elements are identified within the introns of both species, further supporting recent demonstrations that such short motifs and elements exhibit widespread positioning throughout non-coding regions of the genome. This novel characterization of ldh-b in these two coral reef fishes allows for a wide range of future studies (e.g. analytical comparisons of ldh-b and LDH-B among different fish genera from different thermal environments and habitats).     

Refining gene signatures: a Bayesian approach

Background  

In high density arrays, the identification of relevant genes for disease classification is complicated by not only the curse of dimensionality but also the highly correlated nature of the array data. In this paper, we are interested in the question of how many and which genes should be selected for a disease class prediction. Our work consists of a Bayesian supervised statistical learning approach to refine gene signatures with a regularization which penalizes for the correlation between the variables selected.     

An antisense-based functional genomics approach for identification of genes critical for growth of Candida albicans

Converting the complete genome sequence of Candida albicans into meaningful biological information will require comprehensive screens for identifying functional classes of genes. Most systems described so far are not applicable to C. albicans because of its difficulty with mating, its diploid nature, and the lack of functional random insertional mutagenesis methods. We examined artificial gene suppression as a means to identify gene products critical for growth of this pathogen; these represent new antifungal drug targets. To achieve gene suppression we combined antisense RNA inhibition and promoter interference. After cloning antisense complementary DNA (cDNA) fragments under control of an inducible GAL1 promoter, we transferred the resulting libraries to C. albicans. Over 2,000 transformant colonies were screened for a promoter-induced diminished-growth phenotype. After recovery of the plasmids, sequence determination of their inserts revealed the messenger RNA (mRNA) they inhibited or the gene they disrupted. Eighty-six genes critical for growth were identified, 45 with unknown function. When used in high-throughput screening for antifungals, the crippled C. albicans strains generated in this study showed enhanced sensitivity to specific drugs.     

A functional genomics approach to evaluate candidate genes located in a QTL interval for milk production traits on BTA6

The potential genetic and economic advantage of marker-assisted selection for enhanced production in dairy cattle has provided an impetus to conduct numerous genome scans in order to identify associations between DNA markers and future productive potential. One area of focus has been a quantitative trait locus on bovine chromosome 6 (BTA6) found to be associated with milk yield, milk protein and fat percentage, which has been subsequently fine-mapped to six positional candidate genes. Subsequent investigations have yet to resolve which of the potential positional candidate genes is responsible for the observed associations with productive performance. In this study, we analysed candidate gene expression and the effects of gene knockdown on expression of β- and κ-casein mRNA in a small interfering RNA transfected bovine in vitro mammosphere model. From our expression studies in vivo , we observed that four of the six candidates ( ABCG2 , SPP1 , PKD2 and LAP3 ) exhibited differential expression in bovine mammary tissue over the lactation cycle, but in vitro functional studies indicate that inhibition of only one gene, SPP1 , had a significant impact on milk protein gene expression. These data suggest that the gene product of SPP1 (also known as osteopontin) has a significant role in the modulation of milk protein gene expression. While these findings do not exclude other positional candidates from influencing lactation, they support the hypothesis that the gene product of SPP1 is a significant lactational regulatory molecule.     

Zebrafish models for the functional genomics of neurogenetic disorders

In this review, we consider recent work using zebrafish to validate and study the functional consequences of mutations of human genes implicated in a broad range of degenerative and developmental disorders of the brain and spinal cord. Also we present technical considerations for those wishing to study their own genes of interest by taking advantage of this easily manipulated and clinically relevant model organism. Zebrafish permit mutational analyses of genetic function (gain or loss of function) and the rapid validation of human variants as pathological mutations. In particular, neural degeneration can be characterized at genetic, cellular, functional, and behavioral levels. Zebrafish have been used to knock down or express mutations in zebrafish homologs of human genes and to directly express human genes bearing mutations related to neurodegenerative disorders such as spinal muscular atrophy, ataxia, hereditary spastic paraplegia, amyotrophic lateral sclerosis (ALS), epilepsy, Huntington's disease, Parkinson's disease, fronto-temporal dementia, and Alzheimer's disease. More recently, we have been using zebrafish to validate mutations of synaptic genes discovered by large-scale genomic approaches in developmental disorders such as autism, schizophrenia, and non-syndromic mental retardation. Advances in zebrafish genetics such as multigenic analyses and chemical genetics now offer a unique potential for disease research. Thus, zebrafish hold much promise for advancing the functional genomics of human diseases, the understanding of the genetics and cell biology of degenerative and developmental disorders, and the discovery of therapeutics. This article is part of a Special Issue entitled Zebrafish Models of Neurological Diseases.     

致病基因的定位候选克隆

基因组研究的迅猛发展,使我们有必要重新审视致病基因克隆的各种策略与技术,以及人类基因组研究在致病基因克隆中的作用。定位候选克隆基因策略强调充分利用已知的细胞遗传学、医学遗传学、分子遗传学、分子生物学和生物化学知识,特别是人类基因组研究的最新成果,综合功能克隆、定位克隆与传统候选基因研究的策略,分离鉴定致病基因。今天的定位克隆已几乎不再需要染色体步移,甚至有可能避开cDNA筛选。     

Bayesian model averaging for evaluation of candidate gene effects

Statistical assessment of candidate gene effects can be viewed as a problem of variable selection and model comparison. Given a certain number of genes to be considered, many possible models may fit to the data well, each including a specific set of gene effects and possibly their interactions. The question arises as to which of these models is most plausible. Inference about candidate gene effects based on a specific model ignores uncertainty about model choice. Here, a Bayesian model averaging approach is proposed for evaluation of candidate gene effects. The method is implemented through simultaneous sampling of multiple models. By averaging over a set of competing models, the Bayesian model averaging approach incorporates model uncertainty into inferences about candidate gene effects. Features of the method are demonstrated using a simulated data set with ten candidate genes under consideration.     

Identification of metastasis genes by a functional genomics approach

Comment on: Gumireddy K, et al. Nat Cell Biol 2009; 11:1297-304.     

Molecular analysis of congenital scoliosis: a candidate gene approach

The etiology of congenital scoliosis is largely unknown. The severe vertebral disorder, spondylocostal dysostosis type 1, is associated with a homozygous delta-like 3 (DLL3) mutation. Scoliosis has been observed in a heterozygous DLL3 carrier, raising the possibility of its involvement in congenital scoliosis. We present the first molecular study of congenital scoliosis by analysis of the candidate gene DLL3 and demonstrate one novel missense variant. However, no novel or previously described mutations are present in our cohort, indicating that DLL3 mutations may not be a major cause of congenital scoliosis. Additionally, we have evaluated patients with congenital scoliosis not diagnosed with a known syndrome and identified a significant number of associated renal and cardiac anomalies and familial incidence of idiopathic scoliosis in this group.     

The candidate gene approach in plant genetics: a review

The candidate gene (CG) approach has been applied in plant genetics in the past decade for the characterisation and cloning of Mendelian and quantitative trait loci (QTLs). It constitutes a complementary strategy to map-based cloning and insertional mutagenesis. The goal of this paper is to present an overview of CG analyses in plant genetics. CG analysis is based on the hypothesis that known-function genes (the candidate genes) could correspond to loci controlling traits of interest. CGs refer either to cloned genes presumed to affect a given trait (`functional CGs') or to genes suggested by their close proximity on linkage maps to loci controlling the trait (`positional CGs'). In plant genetics, the most common way to identify a CG is to look for map co-segregation between CGs and loci affecting the trait. Statistical association analyses between molecular polymorphisms of the CG and variation in the trait of interest have also been carried out in a few studies. The final validation of a CG will be provided through physiological analyses, genetic transformation and/or sexual complementation. Theoretical and practical applications of validated CGs in plant genetics and breeding are discussed.     

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics

Background  

The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5–6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions.     

Benchmarking ortholog identification methods using functional genomics data

Background  

The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the definition of orthology is incorrectly interpreted as a prediction of proteins that are functionally equivalent across species, while in fact it only defines the existence of a common ancestor for a gene in different species. However, it has been demonstrated that orthologs often reveal significant functional similarity. Therefore, the quality of the orthology prediction is an important factor in the transfer of functional annotations (and other related information). To identify protein pairs with the highest possible functional similarity, it is important to qualify ortholog identification methods.